Clinical trials statistical power

The number of partidpants must be chosen so that the trial wUl have suflEdent statistical power , particularly in the case of confirmatory trials. As a general prindple, the greater the number of data, the greater the confidence there is in demonstrating that a statistical difierence between two groups exists, or not. However, numbers of participants will be constrained by cost considerations, the availability of suitable subjects and, above all, by the ethical prindple that subjects should not be enrolled in clinical trial unless they add scientific value. 

The number of subjects planned to be enrolled, if more than one site the numbers of enrolled subjects projected for each trial site should be specified. Reason for choice of sample size include calculations of the statistical power of the trial, the level of significance to be used and the clinical justification. 

Statistical methods are often employed to determine the study sample size and optimize power. Outlining the methods for calculating sample size and power for clinical trials is beyond the scope of this chapter. Interested readers are referred to texts by Chow and Liu (1998), Hulley and Cummings (1988), and Shuster (1990) for specific information on sample size and power estimation methods. 

SAS has always had and will maintain a central role in the data management, analysis, and reporting of clinical trial data. Because of the strong suite of SAS statistical procedures and the power of Base SAS programming, SAS remains a favorite of statisticians for the analysis of clinical trial data. Several companies have built their clinical trial data management and statistical analysis systems entirely with SAS software. More recently, SAS has offered SAS Drug Development as an industry solution that provides a comprehensive clinical trial analysis and reporting environment compliant with 21 CRF-Part 11. 

Clinical trials generate vast quantities of data, most of which are processed by the sponsor. Assessments should be kept to the minimum that is compatible with the safety and comfort of the subject. Highest priority needs to be given to assessment and recording of primary endpoints, as these will determine the main outcome of the study. The power calculation for sample size should be based on the primary critical endpoint. Quite frequently, trials have two or more evaluable endpoints. It must be stated clearly in the protocol whether the secondary endpoints are to be statistically evaluated, in which case power statements will need to be given, or are simply... 

Studies aimed at gathering feasibility and toxicity data on new treatments are usually referred to as phase I trials. Phase II trials are relatively small studies (typically with sample size less than or about 100 subjects) with the purpose of detecting preliminary evidence of efficacy and safety. Phase III trials are larger studies with enough statistical power to test in a conclusive way specific hypotheses about treatment effects. The term clinical trials is often broadly used to designate phase III trials. 

The clinical endpoint is a clinically meaningful measure of how patients feel, function or survive. Investigator-rated or self-assessed rating instruments are the most frequently used clinical endpoints. A primary endpoint is the main outcome that a study protocol is designed to evaluate. The statistical power and the sample size calculation of a particular trial are determined by the primary endpoint. Depending on the purpose of a study the primary endpoint can be... 

The availability of both comprehensive SNP databases (10) and a plethora of technologies available to determine DNA variants at ever-decreasing costs has enabled the pharmaceutical industry to begin to incorporate germline DNA collection and testing into clinical trials (II). This allows for hypotheses to be developed and tested from the start of phase I testing in humans, when a direct correlation can be made between toxicity, efficacy, and pharmacokinetic variables. Furthermore, this allows the sponsor to pool data Irom several studies to significantly increase the statistical power. 

Many trials combine events in their primary outcome measure. This can produce a useful measure of the overall effect of treatment on all the relevant outcomes, and it usually affords greater statistical power, but the outcome that is most important to a particular patient may be affected differently by treatment than the combined outcome. Composite outcomes also sometimes combine events of very different severity, and treatment effects can be driven by the least important outcome, which is often the most frequent. Equally problematic is the composite of definite clinical events and episodes of hospitalization. The fact that a patient is in a trial will probably affect the likelihood of hospitalization and it will certainly vary between different healthcare systems. 

The list of factors that come into play is indicative of the complexity. For example, the absolute risks of different trials ranges from no improvement while on the drug to actual increased mortality. The length of trials is different antibiotics, 14 days, an Alzheimer drug, at least 24 months, and an osteoporosis drug is 36 to 48 months. The size of trials depends on the improvement one wishes to demonstrate and the natural rate of disease progression. One might only need 30 to 40 patients for a trial on some rare tumor disease, in stroke or sepsis, from 60 to 200 patients, but in cardiovascular medicine or obesity, one needs 2,000 to 10,000 patients to have some idea of efficacy. As a reminder, the key for these clinical trials is Is there a sufficient number of patients who are well characterized and who could enter the trial so that we will have the statistical power to... 

The aim of any clinical trial is to have small Type 1 and n errors and consequently sufficient power to detect a difference between treatments, if it exists. Of the four factors that determine sample size, the power and significance level are chosen to suit the level of risk felt to be appropriate the magnitude of the effect can be estimated from previous experience with drugs of the same or similar action the variabiUty of the measurements is often known from published experiments on the primary endpoint, with or without drug. These data will, however, not be available for novel substances in a new class and frequently the sample size in the early phase of development is chosen on a more arbitrary basis. As an example, a trial that would detect, at the 5% level of statistical significance, a treatment that raised a cure rate from 75% to 85% would require 500 patients for 80% power. 

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